Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML.
This three-day, advanced level course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle.
This course includes presentations, demonstrations, practice labs, discussions, and a capstone project.
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What you’ll learn
This course is designed to teach participants how to:
Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
AWS at Lumify Work
Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.
This course is intended for experienced data scientists who are proficient in ML and deep learning fundamentals. Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
Module 1: Amazon SageMaker Setup and Navigation
Launch SageMaker Studio from the AWS Service Catalog
Navigate the SageMaker Studio UI
Demo 1: SageMaker UI Walkthrough
Lab 1: Launch SageMaker Studio from AWS Service Catalog
Module 2: Data Processing
Use Amazon SageMaker Studio to collect, clean, visualise, analyse, and transform data
Set up a repeatable process for data processing
Use SageMaker to validate that collected data is ML ready
Detect bias in collected data and estimate baseline model accuracy
Lab 2: Analyse and Prepare Data Using SageMaker Data Wrangler
Lab 3: Analyse and Prepare Data at Scale Using Amazon EMR
Lab 4: Data Processing Using SageMaker Processing and the SageMaker Python SDK
Lab 5: Feature Engineering Using SageMaker Feature Store
Module 3: Model Development
Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices
Fine-tune ML models using automatic hyperparameter optimisation capability
Use SageMaker Debugger to surface issues during model development
Demo 2: Autopilot
Lab 6: Track Iterations of Training and Tuning Models Using SageMaker Experiments
Lab 7: Analyse, Detect, and Set Alerts Using SageMaker Debugger
Lab 8: Identify Bias Using SageMaker Clarify
Module 4: Deployment and Inference
Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model
Design and implement a deployment solution that meets inference use case requirements
Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines
Lab 9: Inferencing with SageMaker Studio
Lab 10: Using SageMaker Pipelines and the SageMaker Model Registry with SageMaker Studio
Module 5: Monitoring
Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift
Create a monitoring schedule with a predefined interval
Demo 3: Model Monitoring
Module 6: Managing SageMaker Studio Resources and Updates
List resources that accrue charges
Recall when to shut down instances
Explain how to shut down instances, notebooks, terminals, and kernels
Understand the process to update SageMaker Studio
The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. Students can choose among basic, intermediate, and advanced versions of the instructions.
Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK
Please note: This is an emerging technology course. Course outline is subject to change as needed.
It is recommended that all attendees have the following prior to attending this course:
We've created this e-book to assist you on your cloud journey, from defining the optimal cloud infrastructure and choosing a cloud platform, to security in the cloud and the core challenges in moving to the cloud.
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
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